Testing AI Systems with DeepEval: AI Agents, Chatbots & RAG (Udemy.com)

I took this course because I was already involved in manually testing RAG-based applications and wanted to understand how AI testing could be automated and scaled more effectively. The course helped me explore a much broader testing scope than what is practical through manual testing alone. I learned how automated evaluations can be used to assess areas such as answer relevancy, faithfulness, contextual precision and recall, hallucinations, and response consistency across larger sets of test cases. One of my biggest takeaways was understanding how automated evaluation can complement traditional QA. Manual testing remains valuable for exploratory and business-focused scenarios, while automated evaluations can provide repeatability, measurable quality metrics, better regression coverage, and the ability to evaluate many more RAG responses efficiently. Overall, this course has been very useful in connecting my existing QA experience with AI and RAG testing and has given me practical ideas that I can apply to real-world projects.

Rahul Shetty explains GenAI, LLM evaluation, RAG, AI agents, chatbots, and DeepEval in a very practical and easy-to-understand way. I particularly liked the hands-on approach to evaluation metrics, tracing, RAG evaluation, hallucination detection, agent testing, tool correctness, task completion, and safety evaluation. The practical demonstrations and examples make it much easier to understand how GenAI testing can be applied in real-world projects. The course is especially useful for QA/SDET professionals looking to transition into GenAI and LLM application testing. Rahul's teaching style is clear, structured, and easy to follow. Highly recommended for anyone who wants to build practical skills in GenAI testing and evaluation using DeepEval.

I just completed this course and it genuinely reshaped how I think about testing LLM-based systems. Coming from a traditional UI/API automation background, I found the shift to evaluating non-deterministic systems both challenging and fascinating — and this course bridged that gap perfectly. What stood out: >Building real evaluation suites for LLM agents (tool correctness, task completion, prompt alignment) >RAG pipeline testing including retrieval quality and context precision/recall >Multi-turn conversation evaluation for memory and role consistency Automated safety checks for bias, toxicity, and PII leakage >Using an independent judge LLM to score outputs instead of brittle manual assertions >LangChain tracing to capture tool calls and LLM spans Everything is practical and immediately applicable. Rahul's teaching style is clear and hands-on, with no filler. If you're a QA/test engineer trying to stay relevant in an AI-first world, this is well worth your time. Highly recommended.

Thank you, Sir! I really appreciate your teaching style—you dive straight into the core concepts and explain them multiple times, which ensures everything clicks even if we miss it at first. As a suggestion, please consider creating software development courses as well, rather than focusing only on testing frameworks. Keep up the great work!

I’m a QA engineer and I picked up this course because I wanted something practical for testing AI agents / chatbots / RAG systems (not just high-level “LLMs are non-deterministic” theory). This course delivers exactly that. It starts by clearly explaining how AI testing differs from traditional assertion-based testing and then quickly moves into hands-on evaluation with DeepEval—metrics, goldens (golden datasets), tracing/component-level testing, and custom G-Eval style metrics. What I liked most is the structure: it’s very workflow-oriented. You can see how to go from black-box task completion checks to deeper trace-based analysis when things fail, and the course spends time on reading the evaluation reports (including fail reasons) rather than just showing APIs. The sections on multi-turn chatbot evaluation and RAG-specific metrics (contextual precision/recall, response relevance, factual correctness) felt very relevant to what we face in real projects. Big plus: the course also covers synthetic data generation and safety evaluations (harmful/biased/unsafe outputs), which is often ignored in AI tutorials but is critical from a QA standpoint. And if you’re not strong in Python, the dedicated Python fundamentals section at the end is a helpful bridge. Overall, a solid, job-relevant course for testers/SDETs who want a clear mindset shift plus practical tooling to build repeatable evaluation pipelines for AI apps.

I just finished Testing AI Systems with DeepEval course and honestly, it was exactly what I needed. What I liked most is how practical the course feels. It doesn’t stay stuck in theory—Rahul breaks down how to actually evaluate LLM apps in the real world, whether you’re working on a chatbot, an agent workflow, or a RAG pipeline. The explanations are clear, the pacing is solid, and the examples make it easy to connect the dots and apply things to your own projects. If you’ve ever built an AI feature and felt unsure about how to measure if it’s actually getting better, this course really helps. It gave me a much clearer way to think about testing, setting up evaluations, and improving quality in a structured, repeatable way. Overall, it’s a super useful course, and I’d definitely recommend it to anyone building with LLMs who wants to move from "it seems good" to "we can prove it’s good."

I found this course very interesting and engaging. The concepts are explained in a simple, easy-to-understand manner, making it suitable even for learners who are new to RAG, LLM evaluation, and AI testing. One of the best things about the course is its structure. Each lecture is only around 15–20 minutes long, which makes it easy to fit learning into a busy schedule. You can comfortably complete the course and upskill yourself within a couple of weeks. A special mention to the instructor, Rahul, whose teaching style is excellent. He uses very simple language and practical examples to explain complex topics, making the learning experience smooth and enjoyable. Highly recommended for anyone looking to explore AI testing and RAG/LLM evaluation without feeling overwhelmed!

As a QA Automation Engineer, I knew how to test web applications, APIs, and microservices, but I had no idea how AI Agents and Chatbots should be validated in practice. This course gave me a completely new perspective on AI Testing. What I liked most is that Rahul didn't just explain DeepEval APIs. he focused on the mindset of testing AI systems. The examples covering AI Agents, Chatbots, and RAG applications helped me understand where traditional assertions fail and how evaluation metrics can be used instead. The Python prerequisite section was also helpful for someone coming from a Java background. If you're a tester wondering how to stay relevant in the AI era, this course is a great starting point. It feels like learning Selenium in its early days as an emerging skill that every QA professional should be aware of.

I gave it a 4 out of 5 because RAGAS is deprecated which you do not find out until you’ve already loaded your Pycharm project with code linked to it. He also uses ChatGPT model 4, which in later lessons you find out that RAGAS will not accept. I feel the Instructor should pull this course, update it with possibly DeepEval as an LLM wrapper to replace deprecated RAGAS, then push it back to Udemy. Other than that, the Instructor is a wealth of information especially if you are a QA Analyst or Tester looking to up your game to testing AI and evaluating LLM’s. I 100% recommend this course. One more thing, the downloadable resources he provides are worth the price alone.

It is an average course not much focused on LLM evalution mostly it foced on basic LLM evalution metrics and most of it is optimizing the Pytest framework